18 citations · 36 across the 17 of their papers we have counts for
17 papers
TEXAS: Task-Expert-Aware Supervision for Downstream Mixture-of-Experts LLM Adaptation
Guanzhi Deng, Haibo Wang, Kuan Wu +5
Mixture-of-Experts (MoE) language models route each token through a small subset of experts, making routing patterns useful for identifying task-relevant experts during downstream…
RA-MoE: Routing-Aligned Fine-Tuning for Multilingual Adaptation of Mixture-of-Experts Models
Guanzhi Deng, Kuan Wu, Haibo Wang +6
Mixture-of-Experts (MoE) models enable efficient LLM scaling, yet adapting them to non-English downstream tasks remains challenging. Standard multilingual fine-tuning largely ignor…
Reasoning Meets Personalization: Unleashing the Potential of Large Reasoning Model for Personalized Generation
Sichun Luo, Guanzhi Deng, Jian Xu +4
Personalization is a critical task in modern intelligent systems, with applications spanning diverse domains, including interactions with large language models (LLMs). Recent advan…
RALLRec+: Retrieval Augmented Large Language Model Recommendation with Reasoning
Sichun Luo, Jian Xu, Xiaojie Zhang +4
Large Language Models (LLMs) have been integrated into recommender systems to enhance user behavior comprehension. The Retrieval Augmented Generation (RAG) technique is further inc…
RALLRec: Improving Retrieval Augmented Large Language Model Recommendation with Representation Learning
Jian Xu, Sichun Luo, Xiangyu Chen +3
Large Language Models (LLMs) have been integrated into recommendation systems to enhance user behavior comprehension. The Retrieval Augmented Generation (RAG) technique is further…
Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling
Yuxuan Yao, Han Wu, Mingyang Liu +5
Large language models (LLMs) exhibit varying strengths and weaknesses across different tasks, prompting recent studies to explore the benefits of ensembling models to leverage thei…
Privacy in LLM-based Recommendation: Recent Advances and Future Directions
Sichun Luo, Wei Shao, Yuxuan Yao +9
Nowadays, large language models (LLMs) have been integrated with conventional recommendation models to improve recommendation performance. However, while most of the existing works…
Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Delivery
Yuxuan Yao, Sichun Luo, Haohan Zhao +2
We present CNER-UAV, a fine-grained \textbf{C}hinese \textbf{N}ame \textbf{E}ntity \textbf{R}ecognition dataset specifically designed for the task of address resolution in \textbf{…
Learning From Correctness Without Prompting Makes LLM Efficient Reasoner
Yuxuan Yao, Han Wu, Zhijiang Guo +6
Large language models (LLMs) have demonstrated outstanding performance across various tasks, yet they still exhibit limitations such as hallucination, unfaithful reasoning, and tox…
Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation
Sichun Luo, Yuxuan Yao, Bowei He +9
Conventional recommendation methods have achieved notable advancements by harnessing collaborative or sequential information from user behavior. Recently, large language models (LL…
RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation
Sichun Luo, Bowei He, Haohan Zhao +9
Large Language Models (LLMs) have demonstrated remarkable capabilities and have been extensively deployed across various domains, including recommender systems. Prior research has…
MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning
Ke Wang, Houxing Ren, Aojun Zhou +7
The recently released GPT-4 Code Interpreter has demonstrated remarkable proficiency in solving challenging math problems, primarily attributed to its ability to seamlessly reason…
Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification
Aojun Zhou, Ke Wang, Zimu Lu +8
Recent progress in large language models (LLMs) like GPT-4 and PaLM-2 has brought significant advancements in addressing math reasoning problems. In particular, OpenAI's latest ver…
PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training
Sichun Luo, Yuanzhang Xiao, Xinyi Zhang +3
Federated recommendation systems employ federated learning techniques to safeguard user privacy by transmitting model parameters instead of raw user data between user devices and t…
Towards Communication Efficient and Fair Federated Personalized Sequential Recommendation
Sichun Luo, Yuanzhang Xiao, Yang Liu +2
Federated recommendations leverage the federated learning (FL) techniques to make privacy-preserving recommendations. Though recent success in the federated recommender system, sev…
HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations
Sichun Luo, Xinyi Zhang, Yuanzhang Xiao +1
The recent popularity of edge devices and Artificial Intelligent of Things (AIoT) has driven a new wave of contextual recommendations, such as location based Point of Interest (PoI…
Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation
Sichun Luo, Yuanzhang Xiao, Linqi Song
Federated recommendation applies federated learning techniques in recommendation systems to help protect user privacy by exchanging models instead of raw user data between user dev…